Research
FaceLinkGen: A Re-evaluation of Identity Leakage in Privacy-Preserving Face Recognition and Face Anonymization Systems Using Simple Distillation
arXiv:2602.02914v3 Announce Type: replace Abstract: Privacy-preserving face recognition (PPFR) hides facial appearance while retaining machine recognition, whereas perception-preserving face de-identi
arXiv:2602.02914v3 Announce Type: replace Abstract: Privacy-preserving face recognition (PPFR) hides facial appearance while retaining machine recognition, whereas perception-preserving face de-identification (De-ID) retains human recognizability while blocking face recognition models. We show that both designs preserve identity signals that an adaptive attacker can extract. We introduce FaceLinkGen, a unified distillation attack that learns from paired protected and original images. Across MinusFace, PartialFace, and DecoyFace, FaceLinkGen regenerates faces accepted by Face++ at rates of 81.0--99.0% and by Amazon at rates of 74.9--99.2%. Against four De-ID methods, it raises mixed-gallery Recall@1 to 55.2--89.6% and remains effective with limited paired data. These results establish adaptive evaluation as essential for measuring facial privacy and show that resistance to fixed recognition or reconstruction models does not prevent identity leakage.
Related
- DiffMI: Breaking Face Recognition Privacy via Diffusion-Driven Training-Free Model Inversion
- LBFTI: Layer-Based Facial Template Inversion for Identity-Preserving Fine-Grained Face Reconstruction
- SCOUT: Semantic Concept Discovery for Open-Vocabulary Editing of face Recognition Templates
- Random Erasing vs. Model Inversion: A Promising Defense or a False Hope?
Source: arXiv cs.CV | 2026-09-09